Incorporating Deep Syntactic and Semantic Knowledge for Chinese Sequence Labeling with GCN

Autor:	Tang, Xuemei, Wang, Jun, Su, Qi
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Recently, it is quite common to integrate Chinese sequence labeling results to enhance syntactic and semantic parsing. However, little attention has been paid to the utility of hierarchy and structure information encoded in syntactic and semantic features for Chinese sequence labeling tasks. In this paper, we propose a novel framework to encode syntactic structure features and semantic information for Chinese sequence labeling tasks with graph convolutional networks (GCN). Experiments on five benchmark datasets, including Chinese word segmentation and part-of-speech tagging, demonstrate that our model can effectively improve the performance of Chinese labeling tasks. Comment: 10 pages,3 Figures, 6 Tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.02078 Zobrazit plný text záznamu View this record from Arxiv